class_docstr: Interface for the ICA_AROMA.py script.
  
  ICA-AROMA (i.e. 'ICA-based Automatic Removal Of Motion Artifacts') concerns
  a data-driven method to identify and remove motion-related independent
  components from fMRI data. To that end it exploits a small, but robust
  set of theoretically motivated features, preventing the need for classifier
  re-training and therefore providing direct and easy applicability.
  
  See link for further documentation: https://github.com/rhr-pruim/ICA-AROMA
  
  Example
  -------
  
  >>> from nipype.interfaces.fsl import ICA_AROMA
  >>> from nipype.testing import example_data
  >>> AROMA_obj = ICA_AROMA()
  >>> AROMA_obj.inputs.in_file = 'functional.nii'
  >>> AROMA_obj.inputs.mat_file = 'func_to_struct.mat'
  >>> AROMA_obj.inputs.fnirt_warp_file = 'warpfield.nii'
  >>> AROMA_obj.inputs.motion_parameters = 'fsl_mcflirt_movpar.txt'
  >>> AROMA_obj.inputs.mask = 'mask.nii.gz'
  >>> AROMA_obj.inputs.denoise_type = 'both'
  >>> AROMA_obj.inputs.out_dir = 'ICA_testout'
  >>> AROMA_obj.cmdline  # doctest: +ELLIPSIS
  'ICA_AROMA.py -den both -warp warpfield.nii -i functional.nii -m mask.nii.gz -affmat func_to_struct.mat -mc fsl_mcflirt_movpar.txt -o .../ICA_testout'
code: def _format_arg(self, name, trait_spec, value):
      if name == "out_dir":
          return trait_spec.argstr % os.path.abspath(value)
      return super()._format_arg(name, trait_spec, value)
  
file_path: nipype/nipype/interfaces/fsl/aroma.py
imports: from ..base import TraitedSpec, CommandLineInputSpec, CommandLine, File, Directory, traits, isdefined
  import os
label_desc: Use image node_id 1 for calling the ICA_AROMA obj's underlying member method code with example usage: obj._format_arg(name, trait_spec, value) and returns: super, unknown